Remote geophysical data are likely to include active seismic reflection data; electromagnetic data (either controlled source or magneto-telluric); and/or gravity measurements; however, they may include any type of data that can be used to remotely infer the properties of subsurface rocks in the region of interest. These rock properties can be converted to the geological properties of interest in hydrocarbon exploration (e.g., porosity and fluid type) via some sort of rock physics relationship, which can be embedded in the mathematical equations used to invert the geophysical data. In general, to perform inversions of this type, one must assume a particular rock physics relationship between the geophysical parameters that predict the data and the geological parameters of interest. This assumption generally restricts one to an a priori assumption of the lithology (or class of rocks), that is present in the subsurface. A lithology class is a rock type that is considered to possess unifying rock physics behavior for the purposes of the inversion; e.g. clastics and carbonates might be considered two distinct lithology classes in a particular model each with their own rock physics relationship. However, the lithology in the subsurface of a particular region of interest is often not known beforehand, and, in addition, a single physical volume covered by the data may contain more than one lithology with an unknown distribution of the lithologies.
Current methods for inverting geophysical data for geological parameters and/or lithology generally fall into one of two categories. The first category covers methods where the lithology class is assumed known and an appropriate rock physics model is applied. In this case, if a “lithology” is to be found during the inversion it refers not to a discrete lithologic class as the term is used in this document, but to a lithology parameter that changes the physical nature of the rock in a predictable and continuous manner. For example, the lithology is assumed to be clastic, and part of the inversion involves estimating the percentage of clay in the rock (Vclay). This method is exemplified by Saltzer et al (2008) in which an inversion of seismic data for elastic parameters in the subsurface is performed and then a second inversion for the continuous geologic parameters—porosity and Vclay—is performed.
Statistical methods, the second category of methods for inverting for geological parameters, blur the distinction between lithology classes as a discrete categorical label and lithology. This is possible because a lithology in this method is simply defined as a class of rocks that can be assigned a probability density function (pdf) of continuous parameters (e.g. seismic p-wave velocity, or porosity): no explicit rock physics equations are necessary. The use of the statistical method is demonstrated by Guillen et al (2004) who use gravity and magnetic data to invert for lithology of the subsurface. At each iteration of the inversion, the density and magnetic susceptibility of a resolution cell is chosen randomly from the pdf of the lithology that is currently assigned to that cell. In this case, the lithology class of the rocks is not taken as known before the inversion; instead, the lithology, along with density and magnetization, of the inversion cells can change as the inversion progresses (in this particular example, via use of a Metropolis type Monte Carlo pseudo-random process). A similar example is the inversion of active seismic reflection data for lithology using a Bayesian framework, where again the various lithologies are assigned pdfs, in this case for the elastic parameters (Buland et al. 2008).
The approach of Ruckgaber (1990) is slightly different from the statistical methods described above in that it uses a deterministic selection of lithology after inverting for geophysical properties. This is achieved by dividing the space of inverted geophysical properties into various lithologies with hard boundaries. At every point in the subsurface model the inverted geophysical properties are plotted in this space and the region in which they land determines the lithology for that point.